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Cheng-Yu Wang

Publications and source records attributed to Cheng-Yu Wang.

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Taming Randomness in Random Lasers: Programmable Disorder for Active Control of Random Lasing via Electric-Field-Directed Assembly of Nanowires

Random lasing exploits multiple scattering to provide optical feedback without conventional resonant cavities, enabling simplified architectures that are readily integrated into compact photonic platforms such as wearable sensors and lab-on-chip devices. However, the same disorder that enables cavity-free lasing also makes it challenging to control and tune the emission properties. Here, an electrically reconfigurable random-lasing platform based on dielectrophoretic assembly of chaining silver nanowires suspended in a dye gain medium is reported. An applied electric field across patterned quadrupole electrodes induces nanowire chaining and programmable alignment, enabling real-time reconfiguration of the disorder landscape. Based on the electrically driven disorder state transitions, tunable random-lasing characteristics, including reduced lasing thresholds, modulation of emission intensity, and control of the polarization state have been demonstrated. Simulations further indicate that chaining enhances scattering relative to absorption, providing more efficient radiative feedback, and the orientation of the nanowire network governs the polarization dependence of the system. These results establish a route to actively modulate random lasing through controllable disorder and point toward adaptive, reconfigurable photonic light sources and sensing systems.

physics.optics

Non-line-of-sight imaging off a Phong surface through deep learning

A deep learning based non-line-of-sight (NLOS) imaging system is developed to image an occluded object off a scattering surface. The neural net is trained using only handwritten digits, and yet exhibits capability to reconstruct patterns distinct from the training set, including physical objects. It can also reconstruct a cartoon video from its scattering patterns in real time, demonstrating the robustness and generalization capability of the deep learning based approach. Several scattering surfaces with varying degree of Lambertian and specular contributions were examined experimentally; it is found that for a Lambertian surface the structural similarity index (SSIM) of reconstructed images is about 0.63, while the SSIM obtained from a scattering surface possessing a specular component can be as high as 0.93. A forward model of light transport was developed based on the Phong scattering model. Scattering patterns from Phong surfaces with different degrees of specular contribution were numerically simulated. It is found that a specular contribution of as small as 5% can enhance the SSIM from 0.83 to 0.93, consistent with the results from experimental data. Singular value spectra of the underlying transfer matrix were calculated for various Phong surfaces. As the weight and the shininess factor increase, i.e., the specular contribution increases, the singular value spectrum broadens and the 50-dB bandwidth is increased by more than 4X with a 10% specular contribution, which indicates that at the presence of even a small amount of specular contribution the NLOS measurement can retain significantly more singular value components, leading to higher reconstruction fidelity. With an ordinary camera and incoherent light source, this work enables a low-cost, real-time NLOS imaging system without the need of an explicit physical model of the underlying light transport process.

eess.IV